REAL-TIME AI FEEDBACK FOR DANCE STUDENTS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6798Keywords:
Real-Time Feedback, Computer Vision, Dance Training, Machine Learning, Pose Estimation, Motion AnalysisAbstract [English]
n this study, the authors provide an investigation of how an AI based real-time feedback system can assist dance students to improve the effectiveness of training, precision and student learning outcomes. The system combines computer vision, pose estimation and machine learning algorithms to process motion data obtained by a video or a motion sensor. The AI, by comparing the actions of a dancer with the set reference models, can give immediate corrective feedback through visual overlay or sound and allows learners to dynamically change their posture, timing and rhythm. The paper evaluates some of the available motion-tracking and performance analysis instruments, their weaknesses in terms of latency, contextual interpretation, and suitability to a variety of dance genres. It suggests a solid methodology with the design of the system architecture, data acquisition, and the model training which seems to be concerned with the family of balancing real-time responsiveness with the analytical accuracy. The provided AI model is based on pose estimation frameworks (such as OpenPose or MediaPipe) and trained under the supervision of these algorithms, thus calculating the discrepancy of performance and providing explanatory feedback. In addition, the paper addresses technical and human-based issues, including sensor accuracy, data privacy and user acceptance. The ethical aspects of AI-assisted education are also discussed, and transparency and inclusivity are also regarded as essential. The experimental assessments prove the possibility of this method to transform the dancing instruction into an automated analysis as a way to bridge the existing gap between human and machine-mediated instructions.
References
An, T., and Oliver, M. (2021). What in the World is Educational Technology? Rethinking the Field from the Perspective of the Philosophy of Technology. Learning, Media and Technology, 46(1), 6–19. https://doi.org/10.1080/17439884.2020.1810066 DOI: https://doi.org/10.1080/17439884.2020.1810066
Ben, D., Seunghyun, B., Donal, H., Yongjin, L., and Judy, F. (2024). Students’ Perspectives of Social and Emotional Learning in a High School Physical Education Program. Journal of Teaching in Physical Education, 43(4), 549–556. https://doi.org/10.1123/jtpe.2023-0090 DOI: https://doi.org/10.1123/jtpe.2023-0090
Cao, F., Lei, M., Lin, S., and Xiang, M. (2022). Application of Artificial Intelligence-Based Big Data Technology in Physical Education Reform. Mobile Information Systems, 2022, Article 4017151. https://doi.org/10.1155/2022/4017151 DOI: https://doi.org/10.1155/2022/4017151
Chen, K., Tan, Z., Lei, J., Zhang, S.-H., Guo, Y.-C., Zhang, W., and Hu, S.-M. (2021). ChoreoMaster: Choreography-Oriented Music-Driven Dance Synthesis. ACM Transactions on Graphics, 40(4), Article 1–13. https://doi.org/10.1145/3450626.3459932 DOI: https://doi.org/10.1145/3450626.3459932
Copet, J., Kreuk, F., Gat, I., Remez, T., Kant, D., Synnaeve, G., Adi, Y., and Défossez, A. (2023). Simple and Controllable Music Generation (arXiv:2306.05284). arXiv.
Emma, R., Lewis, S., Miah, A., Lupton, D., and Piwek, L. (2020). Digital Health Generation? Young People’s Use of “Healthy Lifestyle” Technologies. University of Bath.
Gil-Espinosa, F. J., Nielsen-Rodríguez, A., Romance, R., and Burgueño, R. (2022). Smartphone Applications for Physical Activity Promotion from Physical Education. Education and Information Technologies, 27(8), 11759–11779. https://doi.org/10.1007/s10639-022-11108-2 DOI: https://doi.org/10.1007/s10639-022-11108-2
Guo, E., and Cui, X. (2024). Simulation of Optical Sensor Network Based on Edge Computing in Athlete Physical Fitness Monitoring System. Optical and Quantum Electronics, 56, Article 637. https://doi.org/10.1007/s11082-024-07608-9 DOI: https://doi.org/10.1007/s11082-024-06282-1
Hong, C. (2024). Application of Virtual Digital People in the Inheritance and Development of Intangible Cultural Heritage. People’s Forum, 6, 103–105.
Hou, C. (2024). Artificial Intelligence Technology Drives Intelligent Transformation of Music Education. Applied Mathematics and Nonlinear Sciences, 9(1), 21–23. https://doi.org/10.2478/amns-2024-1947 DOI: https://doi.org/10.2478/amns-2024-1947
Julia, S., and Calderón, A. (2021). Technology-Enhanced Learning in Physical Education? A Critical Review of the Literature. Journal of Teaching in Physical Education, 41(4), 689–709. https://doi.org/10.1123/jtpe.2021-0136 DOI: https://doi.org/10.1123/jtpe.2021-0136
Kebao, Z., Kehu, Z., and Liu, W. (2024). The Evaluation of Sports Performance in Tennis Based on Flexible Piezoresistive Pressure Sensing Technology. IEEE Sensors Journal, 24(22), 28111–28118. https://doi.org/10.1109/JSEN.2024.3425591 DOI: https://doi.org/10.1109/JSEN.2024.3425591
Mokmin, N. A. M. (2020). The Effectiveness of a Personalized Virtual Fitness Trainer in Teaching Physical Education by Applying the Artificial Intelligent Algorithm. International Journal of Human Movement and Sports Sciences, 8(5), 258–264. https://doi.org/10.13189/saj.2020.080514 DOI: https://doi.org/10.13189/saj.2020.080514
Mokmin, N. A. M., and Jamiat, N. (2021). The Effectiveness of a Virtual Fitness Trainer App in Motivating and Engaging Students for Fitness Activity by Applying Motor Learning Theory. Education and Information Technologies, 26(2), 1847–1864. https://doi.org/10.1007/s10639-020-10337-7 DOI: https://doi.org/10.1007/s10639-020-10337-7
Wu, J., Gan, W., Chen, Z., Wan, S., and Lin, H. (2023). AI-Generated Content (AIGC): A Survey (arXiv:2304.06632). arXiv.
Yang, Z., Wang, Q., Yu, H., Xu, Q., Li, Y., Cao, M., Dhakal, R., Li, Y., and Yao, Z. (2024). Self-Powered Biomimetic Pressure Sensor Based on Mn–Ag Electrochemical Reaction for Monitoring Rehabilitation Training of Athletes. Advanced Science, 11(15), Article 2401515. https://doi.org/10.1002/advs.202401515 DOI: https://doi.org/10.1002/advs.202401515
Yu, C. W. (2022). A Case Study on Physical Education Class Using Physical Activity Solution App. Journal of Research in Curriculum and Instruction, 26(3), 458–471.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Suraj Bhan, Akhilesh Kumar Khan, Dr. Shruthi K Bekal, Pavas Saini, Abhishek Singla, Kajal Thakuriya, Dipali Kapil Mundada
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
